Bioinformatics Methods for Transcriptome Analysis on Teratogenesis Testing.
Developmental toxicity
Gene expression
Microarray
Pipeline
RNA-Seq
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2024
2024
Historique:
medline:
29
1
2024
pubmed:
29
1
2024
entrez:
29
1
2024
Statut:
ppublish
Résumé
Teratogenesis testing can be challenging due to the limitations of both in vitro and in vivo models. Test-systems, based especially on human embryonic cells, have been helping to overcome the difficulties when allied to omics strategies, such as transcriptomics. In these test-systems, cells exposed to different compounds are then analyzed in microarray or RNA-seq platforms regarding the impacts of the potential teratogens in the gene expression. Nevertheless, microarray and RNA-seq dataset processing requires computational resources and bioinformatics knowledge. Here, a pipeline for microarray and RNA-seq processing is presented, aiming to help researchers from any field to interpret the main transcriptome results, such as differential gene expression, enrichment analysis, and statistical interpretation. This chapter also discusses the main difficulties that can be encountered in a transcriptome analysis and the better alternatives to overcome these issues, describing both programming codes and user-friendly tools. Finally, specific issues in the teratogenesis field, such as time-course analysis, are also described, demonstrating how the pipeline can be applied in these studies.
Identifiants
pubmed: 38285351
doi: 10.1007/978-1-0716-3625-1_20
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
365-376Informations de copyright
© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Mayshar Y, Yanuka O, Benvenisty N (2011) Teratogen screening using transcriptome profiling of differentiating human embryonic stem cells. J Cell Mol Med 15(6):1393–1401
doi: 10.1111/j.1582-4934.2010.01105.x
pubmed: 20561110
Shinde V et al (2016) Comparison of a teratogenic transcriptome-based predictive test based on human embryonic versus inducible pluripotent stem cells. Stem Cell Res Ther 7(1):190
doi: 10.1186/s13287-016-0449-2
pubmed: 28038682
pmcid: 5203708
for, O. and E.C.-o.a. (2001) Development. OECD guideline for the testing of chemicals. https://www.oecd.org/env/ehs/testing/E414_2001.PDF
Sipes NS et al (2011) Predictive models of prenatal developmental toxicity from ToxCast high-throughput screening data. Toxicol Sci 124(1):109–127
doi: 10.1093/toxsci/kfr220
pubmed: 21873373
Kowalski TW et al (2019) Assembling systems biology, embryo development and teratogenesis: What do we know so far and where to go next? Reprod Toxicol 88:67–75
doi: 10.1016/j.reprotox.2019.07.015
pubmed: 31362043
Worley KE et al (2018) Teratogen screening with human pluripotent stem cells. Integr Biol (Camb) 10(9):491–501
doi: 10.1039/c8ib00082d
pubmed: 30095839
Flamier A, Singh S, Rasmussen TP (2018) Use of human embryoid bodies for teratology, vol 75. Curr Protoc Toxicol, pp 13.13.1–13.13.14
Konala VBR et al (2021) Neuronal and cardiac toxicity of pharmacological compounds identified through transcriptomic analysis of human pluripotent stem cell-derived embryoid bodies. Toxicol Appl Pharmacol 433:115792
doi: 10.1016/j.taap.2021.115792
pubmed: 34742744
Colleoni S et al (2014) A comparative transcriptomic study on the effects of valproic acid on two different hESCs lines in a neural teratogenicity test system. Toxicol Lett 231(1):38–44
doi: 10.1016/j.toxlet.2014.08.023
pubmed: 25192806
Pepke S, Wold B, Mortazavi A (2009) Computation for ChIP-seq and RNA-seq studies. Nat Methods 6(11 Suppl):S22–S32
doi: 10.1038/nmeth.1371
pubmed: 19844228
pmcid: 4121056
Hu JX, Zhao H, Zhou HH (2010) False discovery rate control with groups. J Am Stat Assoc 105(491):1215–1227
doi: 10.1198/jasa.2010.tm09329
pubmed: 21931466
pmcid: 3175141
Soneson C, Delorenzi M (2013) A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14:91
doi: 10.1186/1471-2105-14-91
pubmed: 23497356
pmcid: 3608160
Conesa A et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13
doi: 10.1186/s13059-016-0881-8
pubmed: 26813401
pmcid: 4728800
Brandies PA, Hogg CJ (2021) Ten simple rules for getting started with command-line bioinformatics. PLoS Comput Biol:e1008645
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140
doi: 10.1093/bioinformatics/btp616
pubmed: 19910308
Smyth GK (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3
doi: 10.2202/1544-6115.1027
pubmed: 16646809
Carvalho BS, Irizarry RA (2010) A framework for oligonucleotide microarray preprocessing. Bioinformatics 26(19):2363–2367
doi: 10.1093/bioinformatics/btq431
pubmed: 20688976
pmcid: 2944196
Gautier L et al (2004) affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20(3):307–315
doi: 10.1093/bioinformatics/btg405
pubmed: 14960456
Ritchie ME et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47
doi: 10.1093/nar/gkv007
pubmed: 25605792
pmcid: 4402510
Gentleman R (2022) Annotate: annotation for microarrays. Bioconductor:R package
Huber W et al (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18(Suppl 1):S96–S104
doi: 10.1093/bioinformatics/18.suppl_1.S96
pubmed: 12169536
Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3(9):1724–1735
doi: 10.1371/journal.pgen.0030161
pubmed: 17907809
Trapnell C et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578
doi: 10.1038/nprot.2012.016
pubmed: 22383036
pmcid: 3334321
Dozmorov MG (2018) GitHub Statistics as a Measure of the Impact of Open-Source Bioinformatics Software. Front Bioeng Biotechnol 6:198
doi: 10.3389/fbioe.2018.00198
pubmed: 30619845
pmcid: 6306043
Andrews S (2023) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4):357–359
doi: 10.1038/nmeth.1923
pubmed: 22388286
pmcid: 3322381
Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323
doi: 10.1186/1471-2105-12-323
pubmed: 21816040
pmcid: 3163565
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550
doi: 10.1186/s13059-014-0550-8
pubmed: 25516281
pmcid: 4302049
Carlson M (2019) org.Hs.eg.db: genome wide annotation for human. Bioconductor:R package
Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8(2):e1002375
doi: 10.1371/journal.pcbi.1002375
pubmed: 22383865
pmcid: 3285573
Yu G et al (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16(5):284–287
doi: 10.1089/omi.2011.0118
pubmed: 22455463
pmcid: 3339379
Yu G et al (2015) DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31(4):608–609
doi: 10.1093/bioinformatics/btu684
pubmed: 25677125
Carlson M (2016) KEGG.db: a set of annotation maps for KEGG. Bioconductor:R package
Yu G, He QY (2016) ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol BioSyst 12(2):477–479
doi: 10.1039/C5MB00663E
pubmed: 26661513
Luo W, Brouwer C (2013) Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29(14):1830–1831
doi: 10.1093/bioinformatics/btt285
pubmed: 23740750
pmcid: 3702256
Yu G (2022) enrichplot: visualization of functional enrichment result. Bioconductor:R package
Wickham, H., ggplot2: elegant graphics for data analysis, Springer 2016: New York
Law CW et al (2014) voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15(2):R29
doi: 10.1186/gb-2014-15-2-r29
pubmed: 24485249
pmcid: 4053721
Ma S, Huang J (2009) Regularized gene selection in cancer microarray meta-analysis. BMC Bioinformatics 10:1
doi: 10.1186/1471-2105-10-1
pubmed: 19118496
pmcid: 2631520
McCarthy DJ, Chen Y, Smyth GK (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 40(10):4288–4297
doi: 10.1093/nar/gks042
pubmed: 22287627
pmcid: 3378882
Toro-Domínguez D et al (2021) A survey of gene expression meta-analysis: methods and applications. Brief Bioinform 22(2):1694–1705
doi: 10.1093/bib/bbaa019
pubmed: 32095826
Li H et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079
doi: 10.1093/bioinformatics/btp352
pubmed: 19505943
pmcid: 2723002
Liu S et al (2021) Three differential expression analysis methods for RNA sequencing: limma, EdgeR, DESeq2. J Vis Exp 175
Barrett T et al (2013) NCBI GEO: archive for functional genomics data sets – update. Nucleic Acids Res 41(Database issue):D991–D995
pubmed: 23193258
Xia J, Gill EE, Hancock RE (2015) NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc 10(6):823–844
doi: 10.1038/nprot.2015.052
pubmed: 25950236
Afgan E et al (2018) Federated galaxy: biomedical computing at the frontier. IEEE Int Conf Cloud Comput 2018